To compute the log likelihood, we need to get the ratios and use them to compute a score that will allow us to decide whether a tweet is positive or negative. The higher the ratio, the more positive the word is:

To do inference, you can compute the following:
P(neg)P(pos)∏i=1mP(wi∣neg)P(wi∣pos)>1
As m gets larger, we can get numerical flow issues, so we introduce the log, which gives you the following equation:
log(P(neg)P(pos)∏i=1nP(wi∣neg)P(wi∣pos))⇒logP(neg)P(pos)+∑i=1nlogP(wi∣neg)P(wi∣pos)
The first component is called the log prior and the second component is the log likelihood. We further introduce λ as follows:

Having the λ dictionary will help a lot when doing inference.